Multidimensional poverty in Sub-Saharan Africa : levels and trends
Bibliographic record
Abstract
This paper provides an overview of multidimensional poverty – levels and trends – in Sub-Saharan Africa (SSA), using the most recent estimations and analyses of the global Multidimensional Poverty Index (MPI), which was developed by the Oxford Poverty and Human Development Initiative (OPHI), launched in 2010 and reported in UNDP’s Human Development Reports. The global MPI 2014 covers 37 SSA countries, which are home to 91% of the population of the region. This paper synthesizes the main results: the levels of poverty in SSA overall as well as in West, East, Central and Southern Africa. It compares the MPI in rural and urban areas and the MPI with income poverty. It also summarizes results on inequality among the poor as this is highest in SSA countries. In terms of poverty dynamics, of the 19 SSA countries for which we have time-series data, 17 – covering 93% of the poor people across all 19 – had statistically significant reductions in multidimensional poverty. Finally, we scrutinize the situation in SSA according to a new measure of destitution, which identifies a subset of poor people as destitute if they experience a number of extreme deprivations like severe malnutrition or losing two children. Throughout this analysis, the paper demonstrates the descriptive analyses that multidimensional poverty indices enable – such as decomposition and dynamic analysis of poverty by subnational groups and ethnic groups, and the breakdown and dynamic analysis of the composition of the MPI according to its constituent indicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".